Is CDC’s New Fatality Rate Estimate Credible?

Last week the CDC reported that the number of Americans who’ve been infected by the coronavirus exceeds 20 million — an estimate close to my calcs. Now the CDC announces that their “best estimate” for the US covid infection fatality rate (IFR) is around 0.3% — half the rate that I’ve been using.

Assuming that the CDC number is accurate, then the estimated total number of infections in this country would the number of reported deaths divided by the IFR: 129K/.003 = 43 million. That’s twice the CDC’s own infection estimate: what gives?

Maybe the early runaway contagion that hit NY, NJ, and MA has skewed covid mortality estimates toward the high side. Hospitals were severely understaffed and underequipped; doctors weren’t sure how best to treat patients suffering from this new disease; infected nursing home patients were returned from hospitals to nursing homes, exacerbating viral superspread among the most vulnerable demographics. Consider the ratio of total deaths to total test-positive cases: for NY, NJ, and MA combined the ratio is 7.8%; for the rest of the country it’s 3.8%. This discrepancy can’t be explained by differences in testing rates or in population demographics. Those who got hit hardest and earliest have suffered the highest fatality rates.

At least one modeling expert believes that the CDC’s best estimate is overly optimistic, falsely discounting the New York data and painting an overly rosy picture perhaps for political reasons. Still, halving the US fatality rate could explain, at least in part, the anomaly of rising test-positive rates while death rates are on the decline. On the other hand, the nationwide down-sloping death trend began around the second week of May, by which point deaths in NY, NJ, and MA had already dropped considerably from their peaks.

The CDC might be right, but without better data it’s hard to say. It’s shocking that after all this time the CDC still isn’t coordinating systematic well-designed studies for actually measuring the spread of the virus nationwide, rather than having to rely on models extrapolating from localized, sporadic, and in some instances methodologically suspect investigations.

Death as Lagging Indicator

I’ve been using covid death data as a proxy indicator for infections because it’s not subject to the systematic distortions and gross undercounting that characterize confirmed test-positive diagnoses. US covid deaths have decreased substantially over the past month and a half, but obviously death is a lagging indicator of current contagion rates,  as this article emphasizes:

President Donald Trump and Republican governors are pointing to fewer coronavirus deaths to suggest that the worst of the coronavirus pandemic has passed — and to blunt criticism that a surge of new infections in more than half the states is proof the country reopened too soon. But that’s a dangerous gamble. Death rates tell nothing about the current spread of the virus and only offer a snapshot of where the country was roughly three weeks ago.

That’s true enough, but test-positives too are a lagging indicator. In this country testing has typically been reserved for people experiencing symptoms severe and persistent enough for them to seek medical attention. Due to chronic shortages in the supply chain, test results are often delayed by several days. To paraphrase the article: Test-positives offer a snapshot of where the country was roughly two weeks ago.

All else equal, trends in the test-positive rate would manifest themselves a week later in death trends. But over the past month and a half all else has not been equal: diagnoses have been steady or rising, while deaths have been falling. What’s not equal?

  1. Testing rate. All else equal, more tests yield more test-positives.
  2. Clinical practice. Fewer deaths imply fewer severe cases, which opens up the healthcare system’s capacity for diagnosing and treating less severe cases.
  3. Public behavior. If older people stay home while younger people increasingly return to work and to socializing without masks, then new infections shift downward to an age demographic that’s less prone to severe illness and death.

So far the only one of these three un-equalizers that’s been validated empirically is the increased testing rate. There’s anecdotal evidence, supplemented by some localized data, supporting a downward age shift in hospitalizations. It’s not yet clear whether this trend is due to increases in actual infection rates among the younger demographic, or to an expansion in medical capacity toward treating less severe cases, which in turn would result in a younger patient population.

The worst might be over, as the Administration claims, or – more likely — the US has just witnessed the ebbing of its first wave and the beginning of a second. It’s possible to envision a strategy behind Trump and company’s recklessness. Is he going for herd immunity? Is it more important to keep Americans free than to keep them alive — is the exercise of everyone’s personal liberty to move about the country unencumbered by social distancing and masks more important than slowing or stopping the epidemic? Is it the economy, stupid? Or is it just plain stupid? Any way you look at it, whatever limited successes America has experienced in dealing with its public health crisis, and however many illnesses have been prevented and lives spared, these modest accomplishments have been achieved despite Trump’s interventions.